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EXAONE 3.0 Model Support (#1258)

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Co-authored-by: Yineng Zhang <me@zhyncs.com>
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2024-08-30 17:08:28 +09:00
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python/sglang/srt/configs/__init__.py Normal file
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from sglang.srt.configs.exaone import ExaoneConfig
__all__ = [
"ExaoneConfig",
]

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python/sglang/srt/configs/exaone.py Normal file
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# coding=utf-8
# Copyright 2024 The LG AI Research EXAONE Lab. All rights reserved.
# Copyright 2024 The LG CNS AI Engineering Team.
# Copyright 2023-2024 SGLang Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" EXAONE model configuration """
from typing import Any, Dict
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: Dict[str, Any] = {}
# ruff: noqa: E501
class ExaoneConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ExaoneModel`. It is used to
instantiate a EXAONE model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Exaone
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 102400):
Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.ExaoneModel`. Vocabulary size of the model.
Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
:class:`~transformers.EXAONEModel`.
max_position_embeddings (:obj:`int`, `optional`, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_size (:obj:`int`, `optional`, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (:obj:`int`, `optional`, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (:obj:`int`, `optional`):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
intermediate_size (:obj:`int`, `optional`, defaults to `hidden_size * 4`):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"silu"`):
The non-linear activation function (function or string) in the decoder.
rope_theta (:obj:`float`, `optional`, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (:obj:`Dict`, `optional`):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (:obj:`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (:obj:`float`, `optional`):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (:obj:`int`, `optional`):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (:obj:`float`, `optional`):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (:obj:`float`, `optional`):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (:obj:`float`, `optional`):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (:obj:`List[float]`, `optional`):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (:obj:`List[float]`, `optional`):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (:obj:`float`, `optional`):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (:obj:`float`, `optional`):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
embed_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for the attention probabilities.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
The epsilon used by the layer normalization layers.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if ``configs.is_decoder=True``.
bos_token_id (:obj:`int`, `optional`, defaults to 0):
Beginning of stream token id.
eos_token_id (:obj:`int`, `optional`, defaults to 2):
End of stream token id.
tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to tie weight embeddings
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
Example::
>>> from transformers import EXAONEModel, ExaoneConfig
>>> # Initializing a EXAONE configuration
>>> configuration = ExaoneConfig()
>>> # Initializing a model from configuration
>>> model = EXAONEModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configs
"""
model_type = "exaone"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=102400,
max_position_embeddings=2048,
hidden_size=2048,
num_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
intermediate_size=None,
activation_function="silu",
rope_theta=10000.0,
rope_scaling=None,
embed_dropout=0.0,
attention_dropout=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=0,
eos_token_id=2,
tie_word_embeddings=True,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_layers
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
if intermediate_size:
self.intermediate_size = intermediate_size
else:
self.intermediate_size = hidden_size * 4
self.activation_function = activation_function
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs
)